Amita Kamath

Computer Vision and Pattern Recognition · 2020–2023

A researcher in the Science corpus: 4 papers; trusts 776 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 166 citations into it; #8,641 of 21,575 by trust here, #10,500 by citations
• Artificial Intelligence — 157 citations into it; #13,113 of 35,345 by trust here, #15,542 by citations
• Multimodal Learning — 71 citations into it; #1,117 of 1,211 by trust here, #1,104 by citations
• Natural Language Processing — 55 citations into it; #2,867 of 5,688 by trust here, #4,361 by citations
• Evaluation & Benchmarks — 40 citations into it
• Model Architectures — 17 citations into it
• Representation & Self-Supervised Learning — 17 citations into it
• Safety, Robustness & Interpretability — 13 citations into it

Whom Amita Kamath trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Kaiming He
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Alex Krizhevsky
5. Yoshua Bengio
6. Ross B. Girshick
7. Ilya Sutskever
8. Yann LeCun
9. Karen Simonyan
10. Andrew Zisserman

Who trusts Amita Kamath overall

1. Xi Ye
2. Greg Durrett
3. Jiasen Lu
4. Neeraj Varshney
5. Jifan Chen
6. Favyen Bastani
7. Christopher Clark
8. Zhengbao Jiang
9. Jacob Eisenstein
10. Dustin Schwenk

Papers from Amita Kamath's perspective

Ordered by how strongly the researchers Amita Kamath trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Selective Question Answering under Domain Shift (2020) — 272 citations
• Towards General Purpose Vision Systems: An End-to-End Task-Agnostic Vision-Language Architecture (2021) — 58 citations
• Webly Supervised Concept Expansion for General Purpose Vision Models (2022) — 72 citations
• What's "up" with vision-language models? Investigating their struggle with spatial reasoning (2023) — 301 citations